Plug-in electric vehicles (PEVs), such as plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs), have drawn interest from state and local governments, automakers, and the general public due to the potential to reduce fossil fuel consumption, tailpipe emissions, overall greenhouse gas emissions, and operating cost. For example, the California Advanced Clean Cars program mandates 1.4 million zero-emission and PHEVs in California by 2025. Although, there has been great research efforts in evaluating PEV benefits quantitatively, one of the obstacles for large deployment and/or acceptance of PEVs is the shortage of charging infrastructure or electric vehicle supply equipment (EVSE) and corresponding charging protocols. The state and local governments, as well as automakers, have shown interest in building a sufficient charging network.
Charging PEVs can increase the electric demand and can have a major impact on the electricity demand curve, as PEV penetration becomes significant. The power requirement of a large number of PEVs at peak or near peak times can lead to significant challenges in cost, delivery through electric grid, and even in generation and ramping capacities. Generally, it is desirable to have the electricity demand and generation of the grid balanced at all times to assure operational stability. Renewable energy generation, such as wind and solar energy generation, may be taken into consideration as negative demand since the power cannot be controlled in the same way as other forms of generation. Thus, the net load, total demand minus renewable generation, may be targeted to be flat or at least slowly varying. A substantially constant (or flat) demand curve is considered beneficial for cost and environmental consideration.
Previous work on analysis of the allocation of charging infrastructure has shown that with large PEV penetration, even with a reliable charging network, the majority of the charging activities occur at home with the current PEV characteristics and charging rates, due to the cheap night time residential electricity and the long dwelling time needed. Unlike daytime charging, overnight charging can be flexible and can be managed so that, aggregated with overall demand, it can result in lower generation cost and emissions. Furthermore, charging time strategy has been shown to have the most significant impact on charging cost reduction and overall grid operation.
Generally, the main goal is to schedule and shift the charging demand of the PEVs to the late evening and very early morning when the overall demand is the lowest. Valley filling is aimed at leveling the overall demand to reduce the need for shutting down and restarting of large power plants. In addition, costs associated with ramping and other factors may be considered when finding an optimal solution for charging cost reduction and efficient overall grid operation. Several techniques have been developed to solve the global valley filling problem through a decentralized and iterative approach. One technique requires the total number of PEVs be available for participation in the iterations needed in the optimization. Such an iterative approach may require significant communication if the number of vehicles becomes large. More crucially, these techniques do not ensure each PEV is charging at the maximum charging rate. Another technique attempts to address the last concern by relying on a stochastic approach in which the start of the charging period is the decision variable in the optimization problem, given the charging rate and state of charge (SOC)—which yields the charging duration. Under mild assumptions, the iterative algorithm converges with probability one. Yet, there are other techniques suggesting decentralized charging controls for PHEV to avoid transformer overloading, but cannot fill the overnight demand valley.
Thus, there is a need in the art for a method of coordinating plug-in electric vehicle charging with electric grid, that can minimize the amount of communication needed between the PEVs and the grid operator and does not require availability of all PEVs for initiating charging time assignments. Moreover, the method of coordinating PEV charging with electric grid can be modified to obtain a charging pattern so that a final net load curve follows a target load curve, for example, to achieve valley filling or ramp rate reduction.